3 citations · 8 across the 8 of their papers we have counts for
8 papers
How Language Models Process Negation
Zhejian Zhou, Tianyi Zhou, Robin Jia +1
We study how Large Language Models (LLMs) process negation mechanistically. First, we establish that even though open-weight models often provide wrong answers to questions involvi…
Conceptual Steganography
Zhejian Zhou, Jonathan May
Language Models (LMs) emit Chains-of-Thought (CoTs) that drive much of their capability. However, the same sequence that carries useful reasoning can also covertly convey messages:…
A Comprehensive Survey on Long Context Language Modeling
Jiaheng Liu, Dawei Zhu, Zhiqi Bai +34
Efficient processing of long contexts has been a persistent pursuit in Natural Language Processing. With the growing number of long documents, dialogues, and other textual data, it…
InternLM2.5-StepProver: Advancing Automated Theorem Proving via Critic-Guided Search
Zijian Wu, Suozhi Huang, Zhejian Zhou +5
Large Language Models (LLMs) have emerged as powerful tools in mathematical theorem proving, particularly when utilizing formal languages such as LEAN. A prevalent proof method inv…
Scaling Behavior for Large Language Models regarding Numeral Systems: An Example using Pythia
Zhejian Zhou, Jiayu Wang, Dahua Lin +1
Though Large Language Models (LLMs) have shown remarkable abilities in mathematics reasoning, they are still struggling with performing numeric operations accurately, such as addit…
StackSight: Unveiling WebAssembly through Large Language Models and Neurosymbolic Chain-of-Thought Decompilation
Weike Fang, Zhejian Zhou, Junzhou He +1
WebAssembly enables near-native execution in web applications and is increasingly adopted for tasks that demand high performance and robust security. However, its assembly-like syn…